Summary of Taste: Teaching Large Language Models to Translate Through Self-reflection, by Yutong Wang et al.
TasTe: Teaching Large Language Models to Translate through Self-Reflection
by Yutong Wang, Jiali Zeng, Xuebo Liu, Fandong Meng, Jie Zhou, Min Zhang
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers investigate the potential of large language models (LLMs) in machine translation. While LLMs have shown impressive performance in various natural language processing tasks, their translation outputs often fall short of supervised neural machine translation systems. The authors propose a new approach called TasTe, which involves a self-reflection process that enables LLMs to refine preliminary translations based on evaluation results. The proposed framework is tested on the WMT22 benchmark and outperforms existing methods in four language directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are really good at doing tasks like translating text from one language to another. But they’re not as good as some other systems that are specifically designed for translation. The authors of this paper tried to figure out why that is and came up with a new way called TasTe. It’s like having the LLM think about its own translations and try to make them better. And it seems to work! They tested it on some language translation tasks and it did better than other methods. |
Keywords
» Artificial intelligence » Natural language processing » Supervised » Translation
